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The antecedent(s) of the 3ms verbal forms and 3ms pronouns in Ps 7:13–14 have long puzzled scholars, stimulating a wide range of interpretations. This article proposes a new solution, that YHWH is the subject of the first line of verse 13 and the remainder of the 3ms forms in verses 13–14 refer to the enemy of the psalmist. This solution accounts for the available data of grammar and syntax in addition to the context of the overall poem, where the description of the enemy and the actions of YHWH have verbal and thematic echoes with the content in vv. 13–14. [ABSTRACT FROM AUTHOR]

In deep learning (DL) frameworks, a checkpoint operation is widely used to store intermediate variable values (e.g., weights, biases, and gradients) on storage media. This operation helps to reduce the recovery time of running a machine learning (ML) model after sudden power failures or random crashes. However, the checkpoint operation can stall the overall training step of the running model and waste expensive hardware resources by leaving the GPU in idle sleep during the checkpoint operation. In addition, the completion time of the checkpoint operation is unpredictable in cloud server environments (e.g., AWS and Azure) because excessive I/O operations issued by other running applications interfere with the checkpoint operations in the storage stacks. To efficiently address the above two problems, we carefully designed Check-QZP, which reduces the amount of data required for checkpoint operations and parallelizes executions on the CPU and GPU by understanding the internal behaviors of the training step. For the evaluation, we implemented Check-QZP and compared it with the traditional approach in real-world multi-tenant scenarios. In the evaluation, Check-QZP outperformed the baseline in all cases in terms of the overall checkpoint time and the amount of data generated by the checkpoint operations, reducing them by up to 87.5% and 99.8%, respectively. In addition, Check-QZP achieved superior training speeds compared to the baseline. [ABSTRACT FROM AUTHOR]

The metachromatic dye dimethylmethylene blue is used to quantify total glycosaminoglycans in urine. Understanding the interaction of dimethylmethylene blue with glycosaminoglycans is pertinent to optimize the assay procedure depending on the type of sample and interpret the findings meaningfully. The present spectrophotometric study determined the optimum sample-to-dye ratio, primary wavelength for measuring absorbance, after studying the interaction of two different chondroitin sulfate species (unfractionated chondroitin sulfate from bovine trachea vs. chondroitin sulfate oligosaccharide with degree of polymerization of 12, from shark cartilage) with dimethylmethylene blue. Respective dye-glycosaminoglycan complexes of the two chondroitin sulfate species showed significantly different absorbance maxima, while that of the chondroitin sulfate oligosaccharide was closer to absorbance maxima of urine glycosaminoglycans. The chondroitin sulfate oligosaccharide showed relatively less stable absorbance readings at higher concentrations in the reaction volume. Furthermore, the chondroitin sulfate reference materials exhibited differences in the linearity of standard curves and hence parallelism. Based on the findings, the method was semiautomated on Beckman Coulter D✕C 700 biochemistry analyzer using the chondroitin sulfate oligosaccharide as the standard. The urine glycosaminoglycan concentration obtained was slightly lower but reasonably close to that obtained through the External Quality Assurance (EQA) scheme administrated by ERNDIM (European Research Network, Inherited Disorders of Metabolism). The findings of the present study can be used to guide the dimethylmethylene blue assay optimization, redevelopment efforts, and harmonization across laboratories. The chondroitin sulfate oligosaccharide is better than the unfractionated chondroitin sulfate from bovine trachea due to its absorbance maxima closer to urine glycosaminoglycans. On the other hand, unfractionated chondroitin sulfate exhibit poor parallelism leading to falsely lower urine glycosaminoglycan levels. [ABSTRACT FROM AUTHOR]

The International Council for Harmonization of Technical Requirements for Pharmaceuticals for Human Use (ICH) adopted Guideline M10 entitled "Bioanalytical Method Validation and Study Sample Analysis" in May 2022. In October 2023, approximately one year after the adoption of the ICH M10 guideline, a "Hot Topic" session was held during the AAPS PharmSci 360 meeting to discuss the implementation of the guideline. The session focused on items the bioanalytical community felt were challenging to implement or ambiguous within the guideline. These topics included cross-validation, parallelism, comparative bioavailability studies, combination drug stability, endogenous analyte bioanalysis, and dilution QCs. In addition, the regulatory perspective on the guideline was presented. This report provides a summary of the Hot Topic session. [ABSTRACT FROM AUTHOR]

Bioassays are regulated, analytical methods used to ensure proper activity (potency) of biological products at release and during long-term storage. Potency is commonly reported on a relative basis by comparing and calibrating a concentration–response curve from the test material to that of a reference standard material. The relative potency approach depends on an assumption that the two concentration–response curves exhibit similar (equivalent) shapes, except for a potency shift. In certain circumstances, however, biological factors preclude the similarity assumption, and the traditional approach becomes unworkable. The antibody-mediated cytotoxicity assay is one example where the similarity assumption does not always hold. Other examples also arise in the fields of toxicology and pharmacology. In this work, we present a non-constant mean relative potency approach which averages the relative potency across a common range of the concentration-response curves. The proposed method captures the changing nature of the relative potency into a summary statistic that can be reported for batch calibration and quality control purposes. We provide inferential methods for this statistic and summarize the results of a simulation comparing these methods across a number of non-constant relative potency scenarios and assay conditions. [ABSTRACT FROM AUTHOR]

Genomic selection (GS) can accomplish breeding faster than phenotypic selection. Improving prediction accuracy is the key to promoting GS. To improve the GS prediction accuracy and stability, we introduce parallel convolution to deep learning for GS and call it a parallel neural network for genomic selection (PNNGS). In PNNGS, information passes through convolutions of different kernel sizes in parallel. The convolutions in each branch are connected with residuals. Four different Lp loss functions train PNNGS. Through experiments, the optimal number of parallel paths for rice, sunflower, wheat, and maize is found to be 4, 6, 4, and 3, respectively. Phenotype prediction is performed on 24 cases through ridge-regression best linear unbiased prediction (RRBLUP), random forests (RF), support vector regression (SVR), deep neural network genomic prediction (DNNGP), and PNNGS. Serial DNNGP and parallel PNNGS outperform the other three algorithms. On average, PNNGS prediction accuracy is 0.031 larger than DNNGP prediction accuracy, indicating that parallelism can improve the GS model. Plants are divided into clusters through principal component analysis (PCA) and K-means clustering algorithms. The sample sizes of different clusters vary greatly, indicating that this is unbalanced data. Through stratified sampling, the prediction stability and accuracy of PNNGS are improved. When the training samples are reduced in small clusters, the prediction accuracy of PNNGS decreases significantly. Increasing the sample size of small clusters is critical to improving the prediction accuracy of GS. [ABSTRACT FROM AUTHOR]

Vivid imagery and intricate tapestry of linguistic features are essential to grasp literary significance and appreciation in Dylan Thomas' poems. This study focuses on one of his most significant poems, Before I Knocked for an in-depth investigation. The study is significant in because combining both the theory of foregrounding and the checklist is unique in this type of study. Therefore, the study aims to analyze Thomas' selected poem and demonstrate how and why various linguistic elements are systematically utilized to create value and quality in the poem. Concerning this, the study utilises qualitative descriptive analysis relying on stylistic analysis within which the theory of foregrounding supplemented by Leech and Short's checklist, is applied. Ultimately, the study finds that Thomas has employed linguistic features and foregrounding aspects systematically and coherently to carry meaning and theme and relate the form and the content of his work. The study also recommends integrating linguistic features in literary stylistic analysis is essential for learners and pedagogical purposes. [ABSTRACT FROM AUTHOR]

The article highlights the cognitive and linguistic aspects of Ukrainian humorous discourse during the full-scale Russian invasion, focusing on the process of interference of words of foreign origin. Particular attention is paid to lexemes and their reinterpretation through cognitive patterns, such as availability heuristics, negative thinking, the accessibility cascade, the friend-foe pattern, the effect of contrast and stereotypical expectations. The study reveals a variety of stylistic means of comism that are caused by the interference of words of foreign origin in humorous Ukrainian discourse. The main techniques are irony, allusion, comic innuendo, parallelism, paradox, hyperbole, baphos, gradation, metaphor, and rhetorical figures. These stylistic elements of humor play an important role in creating a comic effect and shaping a humorous statement in wartime. The determined communicative functions of Ukrainian humor fragments relate to maintaining an optimistic moral state, promoting the unanimity of the Ukrainian people in the difficult circumstances of the war, creating a negative image of the enemy, and relieving psychological stress that occurs in people during hostilities, air raids, and shelling. The sample has three American tokens, three English tokens, two German tokens, and one Turkish token, which is duplicated in two jokes. All of them are reinterpreted and receive additional meanings and connotations. Ukrainian humorous discourse during the full-scale Russian invasion of Ukraine is based on topics relevant to the country: political, military, cultural-historical, and social. The systematized use of borrowed words in modern jokes can be seen, and it concerns the current realities of Ukrainians. Humor itself is built on situational circumstances, military aspects, and their consequences for society. The interference of foreign-language lexemes leads to the acquisition of specific words and phrases with new and ambiguous lexical and semantic meanings. Most of the sample, which refers to the military aid of Western countries, interprets the borrowed words in a positive sense; the other part, which concerns Russian realities, gives the constructions negative comic and ironic-humorous meanings.

In many fields including aerospace, automotive, and telecommunications, MathWorks' MATLAB/Simulink is contemporary standard for model-based design. The strengths of Simulink are rapid design and algorithm exploration. Models created with Simulink are just functional. Therefore, designers cannot effortlessly consider a Simulink model's architecture. As current architectures are optimized to run on multicore processors, software running on these processors needs to be parallelized in order to benefit from their natural performance. For instance, designers need to understand how a Simulink model could be parallelized and how an adequate multicore architecture is selected. This paper focuses on the dataflow-based parallelization of Simulink models and proposes a method based on dataflow to measure the performance of parallelized Simulink models running on multicore architectures. Throughout the parallelization process, the model is converted into a Hierarchical Synchronous DataFlow Graph (HSDFG) keeping its original semantics, and each composite node in the graph is flattened. Then, the graph is mapped and scheduled into a multicore architecture with the ultimate objective that minimizes the end-to-end latency. In the experiment of applying the proposed approach to a real Simulink model, latency of the parallelized model could be reduced successfully on a various multi-core architectures. [ABSTRACT FROM AUTHOR]

Genesis 3:16 is arguably the most troubling biblical verse for issues of gender relations and women's roles. It figures prominently in later Jewish and especially Christian sources, and discussions in those texts have influenced subsequent understandings of the verse and of the Eden narrative in which it is embedded. This article engages in a careful reading of the biblical text in order to elucidate its meaning apart from later traditions. Recognizing the poetic character of the four lines of this verse is an important part of the analytical process, as is situating it within the Eden tale. Also, because no text arises in a vacuum, considering the Iron Age context—the world of the Israelite populace, that is, the world behind the text, a world vastly different from our own—provides the requisite socio-historical sensitivity. An awareness of that ancient context means openness to a suggestion about what Gen 3:16 may have meant to its ancient audience. [ABSTRACT FROM AUTHOR]

The development of communication technologies has increased concerns about data security, increasing the prominence of cryptography. Images are one of the most widely shared data, and chaotic ciphers arouse significant interest from researchers, as traditional ciphers are not optimized for image encryption. Chaotic encryption schemes perform well for low-quality images but should be faster for real-time encryption of Full Ultra HD images. In this context, a novel parallel image cipher scheme is proposed to execute in GPU architectures, where the encryption procedure consists of a single kernel, making it different from previous chaotic ciphers and the AES. This new contribution enables our work to achieve a throughput of 130.8 GB/s on a GeForce RTX3070 and 251.6 GB/s on a Tesla V100 GPU, an increase of 37% compared to the AES and 43 times higher than the previously high for chaotic ciphers. The cipher’s security is also verified regarding multiple forms of attacks. [ABSTRACT FROM AUTHOR]

Summary: The t$$ t $$‐admissibility is a min‐max problem that concerns to determine whether a graph G$$ G $$ contains a spanning tree T$$ T $$ in which the distance between any two adjacent vertices of G$$ G $$ is at most t$$ t $$ in T$$ T $$. The stretch index of G$$ G $$, σ(G)$$ \sigma (G) $$, is the smallest t$$ t $$ for which G$$ G $$ is t$$ t $$‐admissible. This problem is in P for t≤2$$ t\le 2 $$, NP‐complete for σ(G)≤t$$ \sigma (G)\le t $$, t≥4$$ t\ge 4 $$, and remaining open for t=3$$ t=3 $$. In a very recent development, Couto et al. (Inf Process Lett, 2022; 177: 106265) introduced both sequential and parallel algorithms for constructing spanning trees. Additionally, they proposed two greedy heuristics for generating a candidate solution tree, but they left unresolved the issue of how to decide between two vertices when both have equal chances of being chosen in a greedy step. This criterion is important, since different branches can yield different stretch indexes. In response to this question, we developed nine new heuristics that use the concept of vertex importance in complex networks. Our research evaluates results on several types of graphs, including Barabási‐Albert, Erdős‐Rényi, Watts‐Strogatz, and bipartite graphs. Furthermore, we introduce a new parallel algorithm that employs a method using induced cycle of the graph to compare its performance with previously proposed algorithms. We develop a deep analysis on the proposed strategies (parallel and heuristics) comparing all of them to the other ones in the literature and as a result we obtain the best results so far in order to obtain exact values (or heuristics) of stretch indexes. [ABSTRACT FROM AUTHOR]

For Hamiltonian systems with non-canonical structure matrices, a new family of fourth-order energy-preserving integrators is presented. The integrators take a form of a combination of Runge–Kutta methods and continuous-stage Runge–Kutta methods and feature a set of free parameters that offer greater flexibility and efficiency. Specifically, we demonstrate that by carefully choosing these free parameters, a simplified Newton iteration applied to the integrators of order four can be parallelizable. This results in faster and more efficient integrators compared with existing fourth-order energy-preserving integrators. [ABSTRACT FROM AUTHOR]

This paper introduces the hardware implementation of Digital Pseudo-Random Number Generators (DPRNG) based on chaotic systems. First, high-performance pipeline hardware architectures, respectively, for the 3D Lorenz, 3D Chua, 3D Rossler, and 3D Chen chaotic systems are designed to increase the operating frequency. The study also includes an examination of the hardware architectures with 32-bit fixed-point and 32-bit single floating-point data precision. Second, hardware architectures of DPRNG based on a single chaotic system and the congruential generator are put forward. Third, a robust DPRNG, that mixes the 3D Lorenz, 3D Chua, 3D Rossler, and 3D Chen chaotic systems is proposed where all designed chaotic systems operate in parallel. This architecture increases pseudo-random numbers space up to 2480. The FPGA implementation of the proposed pipeline hardware architecture of the complex DPRNG can achieve a maximal operating frequency of 192.446 MHz with a high throughput of 73,899.264 Mbps. The NIST 800-22 test suite result indicates that the DPRNG produces high-quality pseudo-random bits. Consequently, the proposed DPRNG is deemed suitable for use in high-speed applications. [ABSTRACT FROM AUTHOR]

The Cumulative constraint is a foundamental global constraint, which naturally arises in a variety of problems related to scheduling with limited resources. Since its introduction, numerous propagation algorithms have been proposed, offering different tradeoffs between computational complexity and filtering power. Such diversity allows the resolution of a wide range of applications. Motivated by the impressive computational power that modern Graphical Processing Units (GPUs) provide, this paper explores the use of GPUs for the propagation of the Cumulative constraint. The paper describes the development of a GPU-Acceletated Propagator (GAP), motivates the design choices, and provides solutions for several design challenges. The implementation is evaluated in comparison with state-of-the-art constraint solvers on different benchmarks from the literature. The results suggest that our approach is competitive, providing strong filtering in a reasonable amount of time. [ABSTRACT FROM AUTHOR]

This study investigates the use of foregrounding that involves deviation and parallelism as a strategy to maintain the identity and disclose racial discrimination in poetry of Langston Hughes, a famous twentieth-century Afro-American poet during Harlem Renaissance. By adopting qualitative approach, researchers selected three different poems from three different volumes of Hughes's poetry by adopting random sampling technique and analyzed them by applying theory of foregrounding presented by Leech (1969). The foregrounding, a stylistic device emphasizing certain linguistic features to create emphasis or deviation from normative language patterns, serves as means for the poets to reclaim agency and articulate their unique experiences, histories, and perspectives. Researchers found that Hughes's poetry presents many foregrounded features upon the different levels, i.e., phonological, graphological, grammatical and semantic. The findings show that these foregrounded features upon different linguistic levels help the poet to maintain his identity and disclose racial discrimination. Therefore, these foregrounding features also help the poet revolt against the racial discrimination. [ABSTRACT FROM AUTHOR]

Many real-life applications are modelled using hierarchical decision-making in which: an upper-level optimisation task is constrained by a lower-level one. Such class of optimisation problems is referred in the literature as Bi-Level Optimisation Problems (BLOPs). Most of the proposed methods tackled the single-objective continuous case adhering to some regularity assumptions. This is at odds with real-world problems which involve mainly discrete variables and expensive objective function evaluations. Besides, the optimisation process becomes exorbitantly time-consuming, especially when optimising several objectives at each level. For this reason, the Multi-objective variant (MBLOP) remains relatively less explored and the number of methods tackling the combinatorial case is much reduced. Motivated by these observations, we propose in this work an elitist decomposition-based evolutionary algorithm to solve MBLOPs, called ECODBEMA. The basic idea of our proposal is to handle, decomposition, elitism and multithreading mechanisms to cope with the MBLOP's high complexity. ECODBEMA is applied to the production–distribution problem and to a sustainable end-of-life products disassembly case-study based on real-data of Aix-en-Provence French city. We compared the optimal solutions of an exact method using CPLEX solver with near-optimal solutions obtained by ECODBEMA. The statistical results show the significant outperformance of ECODBEMA against other multi-objective bi-level optimisation algorithms. [ABSTRACT FROM AUTHOR]

The article outlines a contemporary method for creating software for multi-processor computers. It describes the identification of parallelizable sequential code structures. Three structures were found and then carefully examined. The algorithms used to determine whether or not certain parts of code may be parallelized result from static analysis. The techniques demonstrate how, if possible, existing sequential structures might be transformed into parallel-running programs. A dynamic evaluation is also a part of our process, and it can be used to assess the efficiency of the parallel programs that are developed. As a tool for sequential programs, the algorithms have been implemented in C#. All proposed methods were discussed using a common benchmark.

Evolutionary biology was previously considered a historical science with predictions about evolutionary trajectories believed to be near impossible. The development of high throughput sequencing and data analysis technologies has challenged this belief, and provided an abundance of data that yields novel insights into evolutionary processes. Evolutionary predictions are now increasingly being used to develop fundamental knowledge of evolving systems and/or to demonstrate evolutionary control. Here we investigate the factors that make evolutionary repeatability more or less likely to increase the accuracy of evolutionary predictions. We identify outstanding questions and provide a potential starting point to determine how evolutionary repeatability is affected by genetic relatedness. [ABSTRACT FROM AUTHOR]

In today's data-rich world, one of the most significant challenges is efficiently identifying near-duplicate data, especially when integrating data from various sources. Identifying near-duplicate documents applies to any content and has been widely used to enhance the efficiency of search engines, identify plagiarism or spam, and so on. Even smaller or specialized search engines can benefit from knowledge about near-duplicate documents. Shingling and MinHash are two state-of-the-art approaches to detecting near-duplicate documents. However, there are not many attempts to improve the performance of this locality-sensitive hashing technique. In this research paper, we propose a parallel implementation of the MinHash algorithm for near-duplicate document detection utilizing the immense parallelism offered by general-purpose GPUs. Experimental results show that the GPU-based parallel solution is far more cost-effective than the CPU-based sequential and parallel solutions. [ABSTRACT FROM AUTHOR]

Rare syntactic constructions show an especially strong tendency to be repeated, but some rare constructions exhibit this tendency much more strongly than others. The reasons for this variation are not well understood. This exploratory study examines five rare noun-phrase (NP) expansions in English: (the rich), (a Bob Gates), (architect Julia Morgan), (the jobs data), and (home electronic equipment). Repetition tendencies are very strong in the first and second of these and somewhat strong in the third; in the fourth and fifth they are much weaker, only slightly higher than those of common NP expansions such as (the black dog). To explain this variation, we suggest that constructions may be associated with different types of discourse: constructions with high repetition tendencies tend to occur in persuasive rather than informative discourse. [ABSTRACT FROM AUTHOR]

Benchmarks play an essential role in the performance evaluation of novel research concepts. Their effectiveness diminishes if they fail to exploit the available hardware of the evaluated microprocessor or, more broadly, if they are not consistent in comparing various systems. An empirical analysis of the consecrated Splash-2 benchmarks suite vs. the latest version Splash-4 was performed. It was shown that on a 64-core configuration, half of the simulated benchmarks reach temperatures well beyond the critical threshold of 105 °C, emphasizing the necessity of a multi-objective evaluation from at least the following perspectives: energy consumption, performance, chip temperature, and integration area. During the analysis, it was observed that the cores spend a large amount of time in the idle state, around 45% on average in some configurations. This can be exploited by implementing a predictive dynamic voltage and frequency scaling (DVFS) technique called the Simple Core State Predictor (SCSP) to enhance the Intel Nehalem architecture and to simulate it using Sniper. The aim was to decrease the overall energy consumption by reducing power consumption at core level while maintaining the same performance. More than that, the SCSP technique, which operates with core-level abstract information, was applied in parallel with a Value Predictor (VP) or a Dynamic Instruction Reuse (DIR) technique, which rely on instruction-level information. Using the SCSP alone, a 9.95% reduction in power consumption and an energy reduction of 10.54% were achieved, maintaining the performance. By combining the SCSP with the VP technique, a performance increase of 8.87% was obtained while reducing power and energy consumption by 3.13% and 8.48%, respectively. [ABSTRACT FROM AUTHOR]

The current popularity in the application of artificial intelligence to solve complex problems is growing. The appearance of chats based on artificial intelligence or natural language processing has generated the creation of increasingly large and sophisticated neural network models, which are the basis of current developments in artificial intelligence. These neural networks can be composed of billions of parameters and their training is not feasible without the application of approaches based on parallelism. This paper focuses on studying pipeline parallelism, which is one of the most important types of parallelism used to train neural network models in deep learning. In this study we offer a look at the most important concepts related to the topic and we present a detailed analysis of 3 pipeline parallelism libraries: Torchgpipe, FairScale, and DeepSpeed. We analyze important aspects of these libraries such as their implementation and features. In addition, we evaluated them experimentally, carrying out parallel trainings and taking into account aspects such as the number of stages in the training pipeline and the type of balance.

With the continuous development of mobile robot technology, its application fields are becoming increasingly widespread, and path planning is one of the most important topics in the field of mobile robot research. This paper focused on the study of the path planning problem for mobile robots in a complex environment based on the ant colony optimization (ACO) algorithm. In order to solve the problems of local optimum, susceptibility to deadlocks, and low search efficiency in the traditional ACO algorithm, a novel parallel ACO (PACO) algorithm was proposed. The algorithm constructed a rank-based pheromone updating method to balance exploration space and convergence speed and introduced a hybrid strategy of continuing to work and killing directly to address the problem of deadlocks. Furthermore, in order to efficiently realize the path planning in complex environments, the algorithm first found a better location for decomposing the original problem into two subproblems and then solved them using a parallel programming method-single program multiple data (SPMD)-in MATLAB. In different grid map environments, simulation experiments were carried out. The experimental results showed that on grid maps with scales of 20 $ \times $ 20, 30 $ \times $ 30, and 40 $ \times $ 40 compared to nonparallel ACO algorithms, the proposed PACO algorithm had less loss of solution accuracy but reduced the average total time by 50.71, 46.83 and 46.03%, respectively, demonstrating good solution performance.

The animal genre treated at a high professional level appears in Moldavian SSR in the 1960s. Stylistically, the compositions of Nicolae Gabzulin can be compared to the works of Antoine Louis Barye. It is possible, however, that the creativity of Nicolae Gabzulin was also influenced by the Russian sculptors Nicolai Ivanovich Liberih and Piotr Karlovich Klodt fon Jurghensburg. In his works, Naum Epelbaum initially follows the same path opposite the representations of sculptors from the union republics of the USSR, exponents of various cultures, such as Ivan Semionovich Efimov, Vasilii Alexeevich Vataghin, Dmitrii Vladimirovichi Gorlov, Gheorghii Nicolaevichi Popandopulo, etc. And in the creation of the talented sculptor Vladislav Şevcenco, there are parallels with the works of Roxana Sergheevna Kirilova, Vasilii Alexeevici Vataghin, Olga Vladimirovna Malîşeva, etc. The stylistic implementations in the Moldovan sculptural animal genre contain numerous models of approach from the outside, which local sculptors partially knew and who cultivated the animal genre in Moldavian space.

In deep learning (DL) frameworks, a checkpoint operation is widely used to store intermediate variable values (e.g., weights, biases, and gradients) on storage media. This operation helps to reduce the recovery time of running a machine learning (ML) model after sudden power failures or random crashes. However, the checkpoint operation can stall the overall training step of the running model and waste expensive hardware resources by leaving the GPU in idle sleep during the checkpoint operation. In addition, the completion time of the checkpoint operation is unpredictable in cloud server environments (e.g., AWS and Azure) because excessive I/O operations issued by other running applications interfere with the checkpoint operations in the storage stacks. To efficiently address the above two problems, we carefully designed Check-QZP, which reduces the amount of data required for checkpoint operations and parallelizes executions on the CPU and GPU by understanding the internal behaviors of the training step. For the evaluation, we implemented Check-QZP and compared it with the traditional approach in real-world multi-tenant scenarios. In the evaluation, Check-QZP outperformed the baseline in all cases in terms of the overall checkpoint time and the amount of data generated by the checkpoint operations, reducing them by up to 87.5% and 99.8%, respectively. In addition, Check-QZP achieved superior training speeds compared to the baseline.

Diabetic nephropathy (DN) and diabetic retinopathy (DR), as microvascular complications of diabetes mellitus, are currently the leading causes of endstage renal disease (ESRD) and blindness, respectively, in the adult working population, and they are major public health problems with social and economic burdens. The parallelism between the two in the process of occurrence and development manifests in the high overlap of disease-causing risk factors and pathogenesis, high rates of comorbidity, mutually predictive effects, and partial concordance in the clinical use of medications. However, since the two organs, the eye and the kidney, have their unique internal environment and physiological processes, each with specific influencing molecules, and the target organs have non-parallelism due to different pathological changes and responses to various influencing factors, this article provides an overview of the parallelism and non-parallelism between DN and DR to further recognize the commonalities and differences between the two diseases and provide references for early diagnosis, clinical guidance on the use of medication, and the development of new drugs. [ABSTRACT FROM AUTHOR]

Abstract-Achieving high solution efficiency on conventional sequential computation architecture is a challenging task due to penetration of multiple renewable energy sources (RESs). This challenge has become the bottleneck for the application in real-time grid operation, grid planning and analysis of the large-scale and complicated modern power system. Therefore, this paper proposes a parallel multi-GPU and multi-process Fast Decoupled (FD) method to accelerate the power flow calculation, reducing the system responsive time and guaranteeing real-time performance on a large-scale modern power system. In this paper, two hierarchy architecture, task parallelism and data parallelism, are designed to optimize the FD solver parallelization. Moreover, the GPUDirect technology is employed to enhance efficiency of data transmission and drastically reduce copy overhead. The proposed method in this paper achieves a speedup of 9 × ∼ 33 × , compared to the single GPU on a sample large-scale power system. [ABSTRACT FROM AUTHOR]

In today's technological landscape, the convergence of the Internet of Things (IoT) with various industries showcases the march of progress. This coming together involves combining diverse data streams from different sources and transmitting processed data in real-time. This empowers stakeholders to make quick and informed decisions, especially in areas like smart cities, healthcare, and industrial automation, where efficiency gains are evident. However, with this convergence comes a challenge - the large amount of data generated by IoT devices. This data overload makes processing and transmitting information efficiently a significant hurdle, potentially undermining the benefits of this union. To tackle this issue, we introduce "Beyond Orion," a novel lossless compression method designed to optimize data compression in IoT systems. The algorithm employs advanced techniques such as Lempel Ziv-Welch and Huffman encoding, while also integrating strategies like pipelining, parallelism, and serialization for greater efficiency and lower resource usage. Through rigorous experimentation, we demonstrate the effectiveness of Beyond Orion. The results show impressive data reduction, with up to 99% across various datasets, and compression factors as high as 7694.13. Comparative tests highlight the algorithm's prowess, achieving savings of 72% and compression factor of 3.53. These findings have significant implications. They promise improved data handling, more effective decision-making, and optimized resource allocation across a range of IoT applications. By addressing the challenge of data volume, Beyond Orion emerges as a significant advancement in IoT data management, marking a substantial step towards realizing the full potential of IoT technology. [ABSTRACT FROM AUTHOR]

A primary challenge in isogeny-based cryptography lies in the substantial computational cost associated to computing and evaluating prime-degree isogenies. This computation traditionally relied on Vélu's formulas, an approach with time complexity linear in the degree but which was further enhanced by Bernstein, De Feo, Leroux, and Smith to a square-root complexity. The improved square-root Vélu's formulas exhibit a degree of parallelizability that has not been exploited in major implementations. In this study, we introduce a theoretical framework for parallelizing isogeny computations and provide a proof-of-concept implementation in C with OpenMP. While the parallelization effectiveness exhibits diminishing returns with the number of cores, we still obtain strong results when using a small number of cores. Concretely, our implementation shows that for large degrees it is easy to achieve speedup factors of up to 1.74 , 2.54 , and 3.44 for two, four, and eight cores, respectively. [ABSTRACT FROM AUTHOR]

Poetic diction routinely involves two complementary classes of features: (i) parallelisms, i.e. repetitive patterns (rhyme, metre, alliteration, etc.) that enhance the predictability of upcoming words, and (ii) poetic deviations that challenge standard expectations/predictions regarding regular word form and order. The present study investigated how these two prediction-modulating fundamentals of poetic diction affect the cognitive processing and aesthetic evaluation of poems, humoristic couplets and proverbs. We developed quantitative measures of these two groups of text features. Across the three text genres, higher deviation scores reduced both comprehensibility and aesthetic liking whereas higher parallelism scores enhanced these. The positive effects of parallelism are significantly stronger than the concurrent negative effects of the features of deviation. These results are in accord with the hypothesis that art reception involves an interplay of prediction errors and prediction error minimization, with the latter paving the way for processing fluency and aesthetic liking. This article is part of the theme issue 'Art, aesthetics and predictive processing: theoretical and empirical perspectives'. [ABSTRACT FROM AUTHOR]

Featured Application: This article proposes a novel graphical approach for the the assessment of parallelism of biomarker tests that takes into consideration situations where parallelism is partially lacking. The new approach expands on earlier observations and criticism of the limitations of statistical methods included in the guidelines of regulatory authorities. Researchers in the field concur on emphasising the importance of ensuring the accuracy and reliability; a pertinent point which still remains to be addressed. To this purpose, two primary computational approaches are discussed: (a) statistical assessment and (b) visual assessment. Statistical methods, such as regression analysis and parallelism/non-parallelism indexes, offer precision and objectivity, making them suitable for large datasets and high accuracy requirements. They can detect subtle differences in parallelism that may be missed by visual assessment. However, they assume a linear relationship between analyte concentration and assay response, which may not always hold true. Visual assessment relies on interpreting graphs or charts depicting the biomarker–concentration–response relationship. It is intuitive and can quickly identify gross deviations from partial parallelism, making it useful for screening biomarker assays. Visual assessment may detect non-parallelism due to confounding factors that statistical methods might miss. The graphical method proposed here suggests using partial parallelism plots, which visually depict the relationship between biomarker concentration and assay response for each sample. These plots enable the identification of non-parallelism caused by analytical issues or confounding factors. They assist in determining the optimal range of dilutions for each sample and provide a language that is easily understood by researchers, regulatory authorities, and technicians. For regulatory authorities, this document provides valuable insights into the assessment of partial parallelism for biomarker tests. It highlights the need for both statistical and visual assessment methods to evaluate parallelism accurately. The proposed use of partial parallelism plots can aid in visualising and understanding the relationship between biomarker concentration and assay response. By considering these plots during the evaluation of biomarker assays, regulatory authorities can ensure the accuracy, reliability, and suitability of these tests as trial outcome measures and for clinical use. Demonstrating parallelism in quantitative laboratory tests is crucial to ensure accurate reporting of data and minimise risks to patients. Regulatory authorities make the demonstration of parallelism before clinical use approval mandate. However, achieving statistical parallelism can be arduous, especially when parallelism is limited to a subrange of the data. To address potential biases and confounds, I propose a simple graphical method, the Partial Parallelism Plot, to demonstrate partial parallelism. The proposed method offers ease of understanding, intuitiveness, and graphical simplicity. It enables the graphical assessment of quantitative data risk when parallelism is lacking within a defined range. As parallelism may not be consistent across the entire analytical range, the plots focus on partial parallelism. The method can readily be programmed into graphical applications for enhanced interactivity. By providing a clear graphical representation, the method allows researchers to ascertain the presence of parallelism in laboratory tests, thus aiding in the validation process for trials and clinical applications. [ABSTRACT FROM AUTHOR]

The article outlines a contemporary method for creating software for multi-processor computers. It describes the identification of parallelizable sequential code structures. Three structures were found and then carefully examined. The algorithms used to determine whether or not certain parts of code may be parallelized result from static analysis. The techniques demonstrate how, if possible, existing sequential structures might be transformed into parallel-running programs. A dynamic evaluation is also a part of our process, and it can be used to assess the efficiency of the parallel programs that are developed. As a tool for sequential programs, the algorithms have been implemented in C#. All proposed methods were discussed using a common benchmark. [ABSTRACT FROM AUTHOR]

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In this paper, it is proved that the structure Lie operator of a real hypersurface in a nonflat complex space form is parallel with respect to the generalized Tanaka–Webster connection if and only if it vanishes identically, and hence, this is equivalent to that the real hypersurface is of type (A). [ABSTRACT FROM AUTHOR]

Automatic license plate recognition which has many applications in intelligent transportation systems has three main steps, License Plate Detection (LPD), segmentation, and character recognition. The first step, LPD is the main step. This is because the accuracy of other steps depends on this step. While LPD has many challenging problems such as illumination changing, weather conditions, vehicle position, number of cars in each image, etc. In this paper, an algorithm for LPD is proposed. In the proposed technique, to remove noises from input images mean filtering is used. After that, the differences between the filtered image and input image are computed and it is converted to a black and white image. Finally, after applying edge detection and closing and opening morphological operations, license plate candidates are detected. The experimental results on a real Iranian dataset show that about 96.16% of license plates are localized. In addition, results show that the mean filter which has been used for noise removal is the most time-consuming kernel in the proposed algorithm. This important kernel on the GPU platform using CUDA parallel programming model is implemented. The experiments show that speedups of up to 14.54× and 10.77× for kernel- and application-level are achieved. [ABSTRACT FROM AUTHOR]

The development of e-healthcare systems requires the application of advanced technologies, such as blockchain technology. The main challenge of applying blockchain technology to e-healthcare is to handle the impact of the delay that results from blockchain procedures during the communication and voting phases. The impacts of latency in blockchains negatively influence systems' efficiency, performance, real-time processing, and quality of service. Therefore, this work proposes a modified model of a blockchain that allows delays to be avoided in critical situations in healthcare. Firstly, this work analyzes the specifications of healthcare data and processes to study and classify healthcare transactions according to their nature and sensitivity. Secondly, it introduces the concept of a fair-proof-of-stake consensus protocol for block creation and correctness procedures rather than famous ones such as proof-of-work or proof-of-stake. Thirdly, the work presents a simplified procedure for block verification, where it classifies transactions into three categories according to the time period limit and trustworthiness level. Consequently, there are three kinds of blocks, since every category is stored in a specific kind of block. The ideas of time period limits and trustworthiness fit with critical healthcare situations and the authority levels in healthcare systems. Therefore, we reduce the validation process of the trusted blocks and transactions. All proposed modifications help to reduce computational costs, speed up processing times, and enhance security and privacy. The experimental results show that the total execution time using a modified blockchain is reduced by about 49% compared to traditional blockchain models. Additionally, the number of messages using modified blockchain is reduced by about 53% compared to the traditional blockchain model. [ABSTRACT FROM AUTHOR]

The AllDifferent constraint is a fundamental tool in Constraint Programming. It naturally arises in many problems, from puzzles to scheduling and routing applications. Such popularity has prompted an extensive literature on filtering and propagation for this constraint. This paper investigates the use of General Processing Units (GPUs) to accelerate filtering and propagation. In particular, the paper presents an efficient parallelization of the AllDifferent constraint on GPU, along with an analysis of different design and implementation choices and evaluation of the performance of the resulting system on several benchmarks. [ABSTRACT FROM AUTHOR]

Evolutionary biology was previously considered a historical science with predictions about evolutionary trajectories believed to be near impossible. The development of high throughput sequencing and data analysis technologies has challenged this belief, and provided an abundance of data that yields novel insights into evolutionary processes. Evolutionary predictions are now increasingly being used to develop fundamental knowledge of evolving systems and/or to demonstrate evolutionary control. Here we investigate the factors that make evolutionary repeatability more or less likely to increase the accuracy of evolutionary predictions. We identify outstanding questions and provide a potential starting point to determine how evolutionary repeatability is affected by genetic relatedness.

Abstract In the most popular distributed stream processing frameworks (DSPFs), programs are modeled as a directed acyclic graph. Using this model, a DSPF can benefit from the parallelism capabilities of distributed clusters. Choosing a reasonable number of vertices for each operator and mapping the vertices to the appropriate processing resources significantly affect the overall system performance. Due to the simplicity of the current DSPF schedulers, these frameworks perform poorly on large-scale clusters. In this paper, we present a heterogeneity-aware scheduling algorithm that finds the proper number of the vertices of an application graph and maps them to the most suitable cluster node. We begin with a pre-processing step which allocates the vertices to the given cluster nodes using profiling data. Then, we gradually increase the topology input rate in order to scale up the application graph. Finally, using a CPU utilization model which predicts the CPU workload based on the input rate to vertices and the processing node’s CPU characteristics, we identify the bottlenecked vertices and allocate new instances derived from them to the least utilized processing resource. Our experimental results on Storm Micro-Benchmark show that (1) the prediction model estimate CPU utilization with 92% accuracy. (2) Compared to the default scheduler of Storm, our scheduler provides 7 to 44% throughput enhancement. (3) The proposed method can find the solution within 4% (worst case) of the optimal scheduler, which obtains the best scheduling scenario using an exhaustive search over problem design space.

To meet the demand for the track's geometric parameter detection equipment for train speed and high-speed aerodynamics tests, a zero-calibration gauge device is designed with the centering limit, height adjustment and horizontal display function in this paper. The bending situation of the zero-calibration gauge is analyzed and the processing technology is studied, which ensures the rationality and realizability of the design of zero-calibration gauge. Then the gauge zero value and the parallelism of the working surface of zero-calibration gauge have been experimentally tested. The experimental results show that the parameter of gauge zero value is 1434.829 mm with a standard deviation of 1.4 μm. The parallelism of the two upper working surfaces is 1.1 μm, and the parallelism of the two inner working surfaces is 4 μm. Finally, the uncertainty evaluation of zero-calibration gauge is completed. The measurement uncertainty of gauge zero value is 12 μm and the measurement uncertainty of height difference is 6 μm.

Martin Lenz introduces Spinoza to us as a bundle theorist of mind, for whom there cannot be a single finite mind. Since Spinoza conceives of the mind as the idea of the body, a reinterpretation of his theory of mind will have a bodily concurrence. As a corporeal interlude to Lenz’ Spinozist theory of mind, I enquire what follows for the latter if one takes into account more explicitly the structural complexity of the attribute of extension, expressing itself in the plurality and variability of bodies. While Lenz reads Spinoza through a lens of intersubjectivity and parallelism, my reflection leads to the consideration that the plurality and variability of bodies might call for a transindividual rather than an intersubjective account and for conceiving of body and mind as equal rather than parallel. That this complicates Lenz’ account of the identity of the mind over time is an avail rather than a problem. It invites developing Lenz’ reading beyond what the context of his book allows, helping us to navigate the intricate dynamics in which we strive for our bodies to become more capable, our minds more eternal.

Background A general view in the study of pollination syndromes is that floral traits usually represent convergent floral adaptations to specific functional pollinator groups. However, the definition of convergence is elusive and contradictory in the literature. Is convergence the independent evolution of either the same trait or similar traits with the same function? A review of the concept of convergence in developmental biology and phylogenetic systematics may shed new light in studies of pollination syndromes. Scope The aims of this article are (1) to explore the notion of convergence and other concepts (analogy, homoplasy and parallelism) within the theory and practice of developmental evolution and phylogenetic systematics; (2) to modify the definitions of syndromes in order to embrace the concepts of analogy and convergence; (3) to revisit the bat pollination syndrome in the context of angiosperm phylogeny, with focus on the showy 'petaloid' organs associated with the syndrome; (4) to revisit the genetic-developmental basis of flower colour; (5) to raise evolutionary hypotheses of floral evolution associated with the bat pollination syndrome; and (6) to highlight some of the current frontiers of research on the origin and evolution of flowers and its impact on pollination syndrome studies in the 21st century. Conclusions The inclusion of the concepts of analogy and convergence within the concept of syndromes will constitute a new agenda of inquiry that integrates floral biology, phylogenetic systematics and developmental biology. Phyllostomid and pteropodid bat pollination syndrome traits in eudicots and monocots represent cases of analogous and convergent evolution. Pollination syndromes are a multivariate concept intrinsically related to the understanding of flower organogenesis and evolution. The formulation of hypotheses of pollination syndromes must consider the phylogenetic levels of universality for both plant and animal taxa, flower development, genetics, homology and evolution, and a clear definition of evolutionary concepts, including analogy, convergence, homoplasy and parallelism. [ABSTRACT FROM AUTHOR]

Accurate quantification of biomarkers has always been a challenge for many bioanalytical scientists due to their endogenous nature and low concentration in biological matrices. Different analytical approaches have been developed for quantifying biomarkers including enzyme-linked immunosorbent assay, immunohistochemistry, western blotting, and chromatographic techniques assisted with mass spectrometry. Liquid chromatography–tandem mass spectrometry-based quantification of biomarkers has gained more attention over other traditional techniques due to its higher sensitivity and selectivity. However, the primary challenge lies with this technique includes the unavailability of a blank matrix for method development. To overcome this challenge, different analytical approaches are being developed including surrogate analyte and surrogate matrix approach. Such approaches include quantification of biomarkers in a surrogate matrix or quantification of an isotopically labeled surrogate analyte in an authentic matrix. To demonstrate the authenticity of the surrogate approach, it is mandatory to establish quantitative parallelism through validation employing respective surrogate analytes and surrogate matrices. In this review, different bioanalytical approaches for biomarker quantification and recent advancements in the field aiming for improvement in the specificity of the techniques have been discussed. Liquid chromatography–tandem mass spectrometry-based surrogate approaches for biomarker quantification and significance of parallelism establishment in both surrogate matrix and surrogate analyte-based approaches have been critically discussed. In addition, different methods for demonstrating parallelism in the surrogate method have been explained. [ABSTRACT FROM AUTHOR]

Loop tiling and fusion are two essential transformations in optimizing compilers to enhance the data locality of programs. Existing heuristics either perform loop tiling and fusion in a particular order, missing some of their profitable compositions, or execute ad-hoc implementations for domain-specific applications, calling for a generalized and systematic solution in optimizing compilers. In this article, we present a so-called basteln (an abbreviation for backward slicing of tiled loop nests) strategy in polyhedral compilation to better model the interplay between loop tiling and fusion. The basteln strategy first groups loop nests by preserving their parallelism/tilability and next performs rectangular/parallelogram tiling to the output groups that produce data consumed outside the considered program fragment. The memory footprints required by each tile are then computed, from which the upward exposed data are extracted to determine the tile shapes of the remaining fusion groups. Such a tiling mechanism can construct complex tile shapes imposed by the dependences between these groups, which are further merged by a post-tiling fusion algorithm for enhancing data locality without losing the parallelism/tilability of the output groups. The basteln strategy also takes into account the amount of redundant computations and the fusion of independent groups, exhibiting a general applicability. We integrate the basteln strategy into two optimizing compilers, with one a general-purpose optimizer and the other a domain-specific compiler for deploying deep learning models. The experiments are conducted on CPU, GPU, and a deep learning accelerator to demonstrate the effectiveness of the approach for a wide class of application domains, including deep learning, image processing, sparse matrix computation, and linear algebra. In particular, the basteln strategy achieves a mean speedup of 1.8× over cuBLAS/cuDNN and 1.1× over TVM on GPU when used to optimize deep learning models; it also outperforms PPCG and TVM by 11% and 20%, respectively, when generating code for the deep learning accelerator. [ABSTRACT FROM AUTHOR]